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Community structure detection based on the neighbor node degree information

Author

Listed:
  • Li-Ying Tang

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Sheng-Nan Li

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Jian-Hong Lin

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Qiang Guo

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Jian-Guo Liu

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China†Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China)

Abstract

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Suggested Citation

  • Li-Ying Tang & Sheng-Nan Li & Jian-Hong Lin & Qiang Guo & Jian-Guo Liu, 2016. "Community structure detection based on the neighbor node degree information," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-11, April.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:04:n:s0129183116500467
    DOI: 10.1142/S0129183116500467
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    Citations

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    Cited by:

    1. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    3. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.
    4. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2018. "Experts in Knowledge Networks: Central Positioning and Intelligent Selections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 890-905.
    5. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    6. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

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